One of the primary goals of my ongoing research is to explore the issues
related to efficient parallel code on a distributed network as opposed to a
supercomputer. I chose to use a distributed network to gain varied exposure to parallel interaction.
To do this, I explore several aspects of game theory, in the
context of the game Othello.

The challenges faced in parallel programming on a distributed
network differ
from those faced with a supercomputer in several ways. Primarily, communication
between processors is much slower and less reliable. Thus creating an efficient
program, one that can overcome such pitfalls and delays, is more difficult. At
the same time, workstations are individually more reliable and accessible than
supercomputers, allowing more confidence in response and greater productivity.
Lastly, due to the general accessibility of workstations, there is a wider
range of
software and tools available to optimize and debug programs.

Othello is particularly attractive because, unlike other strategy
board games, it is rather easy to implement while remaining a challenging
game to play. In order to examine the merits of a parallel version of the game,
I used an optimized sequential version of the game for timing comparisons.
Since I am dealing with a game, the benchmark for performance is the actual time
elapsed while the move is chosen because a human player is less inclined to
play a game with long time delays between moves
However, for the
computer to play
a challenging game, it must examine the repercussions of its actions
many moves in
the future.

The minimax algorithm, especially with alpha-beta cutoffs is an
interesting
candidate for parallelization. Like Othello, it is very
straightforward and
and easy to implement, while being computationally intensive and complex. In
addition, because the work is a tree search rather than a homogeneous system, it
provides unique
challenges to parallelization. Such a solution has wide application
due to the number
of pruned tree-searches that occur in a variety of situations.